db-d2/primevul-codebert-embeddings huggingface dataset:
Commit message: Don't constant-fold DT_RESOURCE constants.

PiperOrigin-RevId: 391803952
Change-Id: I0ea3ec31d3e7dfda0f03b4027a237f08d00a3091
CVE: CVE-2021-41204
CWE: CWE-824
Vulnerability description: TensorFlow is an open source platform for machine learning. In affected versions during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Project: tensorflow
File: constant_folding.cc

bool IsConstantFoldable(
    const Node* n,
    const std::unordered_map<string, std::vector<PartialTensorShape>>*
        shape_map,
    const std::function<bool(const Node*)>& consider,
    int64_t max_constant_size_in_bytes,
    std::unordered_map<const Node*, std::vector<Tensor>>*
        shape_replacement_map) {
  if (n->IsConstant()) {
    return true;
  }
  if (MaybeReplaceShapeOp(n, shape_map, shape_replacement_map)) {
    return true;
  }
  if (n->op_def().is_stateful()) {
    return false;
  }
  if (consider && !consider(n)) {
    return false;
  }
  if (shape_map != nullptr) {
    // We can skip the node if an output is known to be oversized.
    auto shape_it = shape_map->find(n->name());
    if (shape_it != shape_map->end()) {
      for (int64_t i = 0; i < shape_it->second.size(); ++i) {
        const auto& out_shape = shape_it->second[i];
        if (out_shape.IsFullyDefined() &&
            out_shape.num_elements() * DataTypeSize(n->output_type(i)) >
                max_constant_size_in_bytes) {
          return false;
        }
      }
    }
  }
  if (n->IsControlFlow() || n->IsSend() || n->IsRecv()) {
    return false;
  }
  // TODO(yuanbyu): For now disable these session handle operations.
  if (n->IsGetSessionHandle() || n->IsGetSessionTensor() ||
      n->IsDeleteSessionTensor()) {
    return false;
  }
  if (n->IsSource()) {
    return false;
  }
  if (n->IsSink()) {
    return false;
  }
  if (n->IsFakeParam()) {
    return false;
  }
  // Since constant-folding runs on the CPU, do not attempt to constant-fold
  // operators that have no CPU kernel. Also implies that we will not
  // constant-fold functions.
  // TODO(phawkins): allow constant-folding for functions; functions may
  // be arbitrarily expensive to execute.
  if (!KernelDefAvailable(DeviceType(DEVICE_CPU), n->def())) {
    return false;
  }
  // Do not constant fold nodes which will be allocated by ScopedAllocator.
  // This is because the constant-folding graph will not contain the
  // `_ScopedAllocator` node, and that is necessary to be able to run a node
  // that will use this allocator.
  if (n->attrs().Find(kScopedAllocatorAttrName) != nullptr) {
    VLOG(2) << "Skip node [" << n->DebugString()
            << "] for constant folding due to scoped allocator";
    return false;
  }
  return true;
}
Commit message: Prevent heap OOB read in TFLite's `gather.cc`.

Passing negative indices is illegal but there was a missing check so that resulted in OOB accesses.

PiperOrigin-RevId: 387231300
Change-Id: I3111b54b2f232638d795be17efc46abe4ede6bf8
CVE: CVE-2021-37687
CWE: CWE-703
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Project: tensorflow
File: gather.cc

TfLiteStatus Gather(const TfLiteGatherParams& params, const TfLiteTensor* input,
                    const TfLiteTensor* positions, TfLiteTensor* output) {
  tflite::GatherParams op_params;
  op_params.axis = params.axis;
  op_params.batch_dims = params.batch_dims;
  optimized_ops::Gather(op_params, GetTensorShape(input),
                        GetTensorData<InputT>(input), GetTensorShape(positions),
                        GetTensorData<PositionsT>(positions),
                        GetTensorShape(output), GetTensorData<InputT>(output));
  return kTfLiteOk;
}
Commit message: Fix FPE in inpace update ops.

PiperOrigin-RevId: 388303197
Change-Id: Ib48309b6213ffe53eba81004b00e889d653e4b83
CVE: CVE-2021-37660
CWE: CWE-369
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Project: tensorflow
File: inplace_ops.cc

  void Compute(OpKernelContext* ctx) override {
    auto x = ctx->input(0);
    auto i = ctx->input(1);
    auto v = ctx->input(2);

    OP_REQUIRES(ctx, TensorShapeUtils::IsVector(i.shape()),
                errors::InvalidArgument("i must be a vector. ",
                                        i.shape().DebugString()));
    OP_REQUIRES(ctx, x.dims() == v.dims(),
                errors::InvalidArgument(
                    "x and v shape doesn't match (ranks differ): ",
                    x.shape().DebugString(), " vs. ", v.shape().DebugString()));
    for (int i = 1; i < x.dims(); ++i) {
      OP_REQUIRES(
          ctx, x.dim_size(i) == v.dim_size(i),
          errors::InvalidArgument("x and v shape doesn't match at index ", i,
                                  " : ", x.shape().DebugString(), " vs. ",
                                  v.shape().DebugString()));
    }
    OP_REQUIRES(ctx, i.dim_size(0) == v.dim_size(0),
                errors::InvalidArgument(
                    "i and x shape doesn't match at index 0: ",
                    i.shape().DebugString(), " vs. ", v.shape().DebugString()));

    Tensor y = x;  // This creates an alias intentionally.
    // Skip processing if tensors are empty.
    if (x.NumElements() > 0 || v.NumElements() > 0) {
      OP_REQUIRES_OK(ctx, DoCompute(ctx, i, v, &y));
    }
    ctx->set_output(0, y);
  }
Commit message: password: Use crypto_memneq() to compare hashes

Cryptographic verifications should be time-constant so that an attacker
cannot get information about the secrets used by observing the system,
so use crypto_memneq() rather than memcmp() to compare password hashes.

Signed-off-by: Sascha Hauer <s.hauer@pengutronix.de>
CVE: CVE-2021-37848
CWE: CWE-200
Vulnerability description: common/password.c in Pengutronix barebox through 2021.07.0 leaks timing information because strncmp is used during hash comparison.

Project: barebox
File: password.c

static int check_passwd(unsigned char *passwd, size_t length)
{
	struct digest *d = NULL;
	unsigned char *passwd1_sum;
	unsigned char *passwd2_sum;
	int ret = 0;
	int hash_len;

	if (IS_ENABLED(CONFIG_PASSWD_CRYPTO_PBKDF2)) {
		hash_len = PBKDF2_LENGTH;
	} else {
		d = digest_alloc(PASSWD_SUM);
		if (!d) {
			pr_err("No such digest: %s\n",
			       PASSWD_SUM ? PASSWD_SUM : "NULL");
			return -ENOENT;
		}

		hash_len = digest_length(d);
	}

	passwd1_sum = calloc(hash_len * 2, sizeof(unsigned char));
	if (!passwd1_sum)
		return -ENOMEM;

	passwd2_sum = passwd1_sum + hash_len;

	if (is_passwd_env_enable())
		ret = read_env_passwd(passwd2_sum, hash_len);
	else if (is_passwd_default_enable())
		ret = read_default_passwd(passwd2_sum, hash_len);
	else
		ret = -EINVAL;

	if (ret < 0)
		goto err;

	if (IS_ENABLED(CONFIG_PASSWD_CRYPTO_PBKDF2)) {
		char *key = passwd2_sum + PBKDF2_SALT_LEN;
		char *salt = passwd2_sum;
		int keylen = PBKDF2_LENGTH - PBKDF2_SALT_LEN;

		ret = pkcs5_pbkdf2_hmac_sha1(passwd, length, salt,
			PBKDF2_SALT_LEN, PBKDF2_COUNT, keylen, passwd1_sum);
		if (ret)
			goto err;

		if (strncmp(passwd1_sum, key, keylen) == 0)
			ret = 1;
	} else {
		ret = digest_digest(d, passwd, length, passwd1_sum);

		if (ret)
			goto err;

		if (strncmp(passwd1_sum, passwd2_sum, hash_len) == 0)
			ret = 1;
	}

err:
	free(passwd1_sum);
	digest_free(d);

	return ret;
}
Commit message: Fix heap OOB in `tf.raw_ops.RaggedGather`

PiperOrigin-RevId: 388355464
Change-Id: If14d96231d1cd7aad7c4d1c22c1bab1576b75717
CVE: CVE-2021-37641
CWE: CWE-369
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Project: tensorflow
File: ragged_gather_op.cc

  void Compute(OpKernelContext* context) override {
    // Get the input Tensors.
    OpInputList params_nested_splits_in;
    OP_REQUIRES_OK(context, context->input_list("params_nested_splits",
                                                &params_nested_splits_in));
    const Tensor& params_dense_values_in =
        context->input(params_nested_splits_in.size());
    const Tensor& indices_in =
        context->input(params_nested_splits_in.size() + 1);

    DCHECK_GT(params_nested_splits_in.size(), 0);  // Enforced by REGISTER_OP.
    SPLITS_TYPE num_params = params_nested_splits_in[0].dim_size(0) - 1;
    OP_REQUIRES_OK(context, ValidateIndices(indices_in, num_params));

    OP_REQUIRES(context, params_dense_values_in.dims() > 0,
                errors::InvalidArgument("params.rank must be nonzero"));
    SPLITS_TYPE num_params_dense_values = params_dense_values_in.dim_size(0);

    // Calculate the `splits`, and store the value slices that we need to
    // copy in `value_slices`.
    std::vector<std::pair<SPLITS_TYPE, SPLITS_TYPE>> value_slices;
    SPLITS_TYPE num_values = 0;
    std::vector<std::vector<SPLITS_TYPE>> out_splits;
    OP_REQUIRES_OK(context, MakeSplits(indices_in, params_nested_splits_in,
                                       num_params_dense_values, &out_splits,
                                       &value_slices, &num_values));

    // Write the output tensors.
    OP_REQUIRES_OK(context, WriteSplits(out_splits, context));
    OP_REQUIRES_OK(context,
                   WriteValues(params_dense_values_in, value_slices,
                               out_splits.size(), num_values, context));
  }
Commit message: [misc] Fix use-after-free in net_bind()

Thanks to Ba Jinsheng for reporting this bug
CVE: CVE-2021-38383
CWE: CWE-416
Vulnerability description: OwnTone (aka owntone-server) through 28.1 has a use-after-free in net_bind() in misc.c.

Project: owntone-server
File: misc.c

net_bind(short unsigned *port, int type, const char *log_service_name)
{
  struct addrinfo hints = { 0 };
  struct addrinfo *servinfo;
  struct addrinfo *ptr;
  const char *cfgaddr;
  char addr[INET6_ADDRSTRLEN];
  char strport[8];
  int yes = 1;
  int no = 0;
  int fd;
  int ret;

  cfgaddr = cfg_getstr(cfg_getsec(cfg, "general"), "bind_address");

  hints.ai_socktype = (type & (SOCK_STREAM | SOCK_DGRAM)); // filter since type can be SOCK_STREAM | SOCK_NONBLOCK
  hints.ai_family = (cfg_getbool(cfg_getsec(cfg, "general"), "ipv6")) ? AF_INET6 : AF_INET;
  hints.ai_flags = cfgaddr ? 0 : AI_PASSIVE;

  snprintf(strport, sizeof(strport), "%hu", *port);
  ret = getaddrinfo(cfgaddr, strport, &hints, &servinfo);
  if (ret < 0)
    {
      DPRINTF(E_LOG, L_MISC, "Failure creating '%s' service, could not resolve '%s' (port %s): %s\n", log_service_name, cfgaddr ? cfgaddr : "(ANY)", strport, gai_strerror(ret));
      return -1;
    }

  for (ptr = servinfo, fd = -1; ptr != NULL; ptr = ptr->ai_next)
    {
      if (fd >= 0)
	close(fd);

      fd = socket(ptr->ai_family, type | SOCK_CLOEXEC, ptr->ai_protocol);
      if (fd < 0)
	continue;

      // TODO libevent sets this, we do the same?
      ret = setsockopt(fd, SOL_SOCKET, SO_KEEPALIVE, &yes, sizeof(yes));
      if (ret < 0)
	continue;

      ret = setsockopt(fd, SOL_SOCKET, SO_REUSEADDR, &yes, sizeof(yes));
      if (ret < 0)
	continue;

      if (ptr->ai_family == AF_INET6)
	{
	  // We want to be sure the service is dual stack
	  ret = setsockopt(fd, IPPROTO_IPV6, IPV6_V6ONLY, &no, sizeof(no));
	  if (ret < 0)
	    continue;
	}

      ret = bind(fd, ptr->ai_addr, ptr->ai_addrlen);
      if (ret < 0)
	continue;

      break;
    }

  freeaddrinfo(servinfo);

  if (!ptr)
    {
      DPRINTF(E_LOG, L_MISC, "Could not create service '%s' with address %s, port %hu: %s\n", log_service_name, cfgaddr ? cfgaddr : "(ANY)", *port, strerror(errno));
      goto error;
    }

  // Get the port that was assigned
  ret = getsockname(fd, ptr->ai_addr, &ptr->ai_addrlen);
  if (ret < 0)
    {
      DPRINTF(E_LOG, L_MISC, "Could not find address of service '%s': %s\n", log_service_name, strerror(errno));
      goto error;
    }

  net_port_get(port, (union net_sockaddr *)ptr->ai_addr);
  net_address_get(addr, sizeof(addr), (union net_sockaddr *)ptr->ai_addr);

  DPRINTF(E_DBG, L_MISC, "Service '%s' bound to %s, port %hu, socket %d\n", log_service_name, addr, *port, fd);

  return fd;

 error:
  close(fd);
  return -1;
}
Commit message: FTP: Fix buffer overflow in PORT parser, reported by Aaron Esau

Signed-off-by: Joachim Nilsson <troglobit@gmail.com>
CVE: CVE-2020-20276
CWE: CWE-787
Vulnerability description: An unauthenticated stack-based buffer overflow vulnerability in common.c's handle_PORT in uftpd FTP server versions 2.10 and earlier can be abused to cause a crash and could potentially lead to remote code execution.

Project: uftpd
File: ftpcmd.c

static void handle_PORT(ctrl_t *ctrl, char *str)
{
	int a, b, c, d, e, f;
	char addr[INET_ADDRSTRLEN];
	struct sockaddr_in sin;

	if (ctrl->data_sd > 0) {
		uev_io_stop(&ctrl->data_watcher);
		close(ctrl->data_sd);
		ctrl->data_sd = -1;
	}

	/* Convert PORT command's argument to IP address + port */
	sscanf(str, "%d,%d,%d,%d,%d,%d", &a, &b, &c, &d, &e, &f);
	sprintf(addr, "%d.%d.%d.%d", a, b, c, d);

	/* Check IPv4 address using inet_aton(), throw away converted result */
	if (!inet_aton(addr, &(sin.sin_addr))) {
		ERR(0, "Invalid address '%s' given to PORT command", addr);
		send_msg(ctrl->sd, "500 Illegal PORT command.\r\n");
		return;
	}

	strlcpy(ctrl->data_address, addr, sizeof(ctrl->data_address));
	ctrl->data_port = e * 256 + f;

	DBG("Client PORT command accepted for %s:%d", ctrl->data_address, ctrl->data_port);
	send_msg(ctrl->sd, "200 PORT command successful.\r\n");
}
Commit message: Merge pull request from GHSA-26j7-ww69-c4qj
CVE: CVE-2022-31031
CWE: CWE-787
Vulnerability description: PJSIP is a free and open source multimedia communication library written in C language implementing standard based protocols such as SIP, SDP, RTP, STUN, TURN, and ICE. In versions prior to and including 2.12.1 a stack buffer overflow vulnerability affects PJSIP users that use STUN in their applications, either by: setting a STUN server in their account/media config in PJSUA/PJSUA2 level, or directly using `pjlib-util/stun_simple` API. A patch is available in commit 450baca which should be included in the next release. There are no known workarounds for this issue.

Project: pjproject
File: stun_simple.c

PJ_DEF(pj_status_t) pjstun_parse_msg( void *buf, pj_size_t buf_len, 
				      pjstun_msg *msg)
{
    pj_uint16_t msg_type, msg_len;
    char *p_attr;

    PJ_CHECK_STACK();

    msg->hdr = (pjstun_msg_hdr*)buf;
    msg_type = pj_ntohs(msg->hdr->type);

    switch (msg_type) {
    case PJSTUN_BINDING_REQUEST:
    case PJSTUN_BINDING_RESPONSE:
    case PJSTUN_BINDING_ERROR_RESPONSE:
    case PJSTUN_SHARED_SECRET_REQUEST:
    case PJSTUN_SHARED_SECRET_RESPONSE:
    case PJSTUN_SHARED_SECRET_ERROR_RESPONSE:
	break;
    default:
	PJ_LOG(4,(THIS_FILE, "Error: unknown msg type %d", msg_type));
	return PJLIB_UTIL_ESTUNINMSGTYPE;
    }

    msg_len = pj_ntohs(msg->hdr->length);
    if (msg_len != buf_len - sizeof(pjstun_msg_hdr)) {
	PJ_LOG(4,(THIS_FILE, "Error: invalid msg_len %d (expecting %d)", 
			     msg_len, buf_len - sizeof(pjstun_msg_hdr)));
	return PJLIB_UTIL_ESTUNINMSGLEN;
    }

    msg->attr_count = 0;
    p_attr = (char*)buf + sizeof(pjstun_msg_hdr);

    while (msg_len > 0) {
	pjstun_attr_hdr **attr = &msg->attr[msg->attr_count];
	pj_uint32_t len;
	pj_uint16_t attr_type;

	*attr = (pjstun_attr_hdr*)p_attr;
	len = pj_ntohs((pj_uint16_t) ((*attr)->length)) + sizeof(pjstun_attr_hdr);
	len = (len + 3) & ~3;

	if (msg_len < len) {
	    PJ_LOG(4,(THIS_FILE, "Error: length mismatch in attr %d", 
				 msg->attr_count));
	    return PJLIB_UTIL_ESTUNINATTRLEN;
	}

	attr_type = pj_ntohs((*attr)->type);
	if (attr_type > PJSTUN_ATTR_REFLECTED_FROM &&
	    attr_type != PJSTUN_ATTR_XOR_MAPPED_ADDR)
	{
	    PJ_LOG(5,(THIS_FILE, "Warning: unknown attr type %x in attr %d. "
				 "Attribute was ignored.",
				 attr_type, msg->attr_count));
	}

	msg_len = (pj_uint16_t)(msg_len - len);
	p_attr += len;
	++msg->attr_count;
    }

    return PJ_SUCCESS;
}
Commit message: Improve to cover scale value greater than one

PiperOrigin-RevId: 433050921
CVE: CVE-2022-29212
CWE: CWE-703
Vulnerability description: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling `QuantizeMultiplierSmallerThanOneExp`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Project: tensorflow
File: comparisons.cc

void ComparisonQuantized(const TfLiteTensor* input1, const TfLiteTensor* input2,
                         TfLiteTensor* output, bool requires_broadcast) {
  if (input1->type == kTfLiteUInt8 || input1->type == kTfLiteInt8) {
    auto input1_offset = -input1->params.zero_point;
    auto input2_offset = -input2->params.zero_point;
    const int left_shift = 8;

    int32 input1_multiplier;
    int input1_shift;
    QuantizeMultiplierSmallerThanOneExp(input1->params.scale,
                                        &input1_multiplier, &input1_shift);
    int32 input2_multiplier;
    int input2_shift;
    QuantizeMultiplierSmallerThanOneExp(input2->params.scale,
                                        &input2_multiplier, &input2_shift);

    ComparisonParams op_params;
    op_params.left_shift = left_shift;
    op_params.input1_offset = input1_offset;
    op_params.input1_multiplier = input1_multiplier;
    op_params.input1_shift = input1_shift;
    op_params.input2_offset = input2_offset;
    op_params.input2_multiplier = input2_multiplier;
    op_params.input2_shift = input2_shift;
    if (requires_broadcast) {
      reference_ops::BroadcastComparison4DSlowWithScaling<input_dtype, opname>(
          op_params, GetTensorShape(input1), GetTensorData<input_dtype>(input1),
          GetTensorShape(input2), GetTensorData<input_dtype>(input2),
          GetTensorShape(output), GetTensorData<bool>(output));
    } else {
      reference_ops::ComparisonWithScaling<input_dtype, opname>(
          op_params, GetTensorShape(input1), GetTensorData<input_dtype>(input1),
          GetTensorShape(input2), GetTensorData<input_dtype>(input2),
          GetTensorShape(output), GetTensorData<bool>(output));
    }
  }
}
Commit message: Fixed recursive async function calls.

Previously, PromiseCapability record was stored (function->context)
directly in function object during a function invocation.  This is
not correct, because PromiseCapability record should be linked to
current execution context.  As a result, function->context is
overwritten with consecutive recursive calls which results in
use-after-free.

This closes #451 issue on Github.
CVE: CVE-2022-25139
CWE: CWE-416
Vulnerability description: njs through 0.7.0, used in NGINX, was discovered to contain a heap use-after-free in njs_await_fulfilled.

Project: njs
File: njs_async.c

njs_await_fulfilled(njs_vm_t *vm, njs_value_t *args, njs_uint_t nargs,
    njs_index_t unused)
{
    njs_int_t           ret;
    njs_value_t         **cur_local, **cur_closures, **cur_temp, *value;
    njs_frame_t         *frame, *async_frame;
    njs_function_t      *function;
    njs_async_ctx_t     *ctx;
    njs_native_frame_t  *top, *async;

    ctx = vm->top_frame->function->context;

    value = njs_arg(args, nargs, 1);
    if (njs_is_error(value)) {
        goto failed;
    }

    async_frame = ctx->await;
    async = &async_frame->native;
    async->previous = vm->top_frame;

    function = async->function;

    cur_local = vm->levels[NJS_LEVEL_LOCAL];
    cur_closures = vm->levels[NJS_LEVEL_CLOSURE];
    cur_temp = vm->levels[NJS_LEVEL_TEMP];
    top = vm->top_frame;
    frame = vm->active_frame;

    vm->levels[NJS_LEVEL_LOCAL] = async->local;
    vm->levels[NJS_LEVEL_CLOSURE] = njs_function_closures(async->function);
    vm->levels[NJS_LEVEL_TEMP] = async->temp;

    vm->top_frame = async;
    vm->active_frame = async_frame;

    *njs_scope_value(vm, ctx->index) = *value;
    vm->retval = *value;

    vm->top_frame->retval = &vm->retval;

    function->context = ctx->capability;
    function->await = ctx;

    ret = njs_vmcode_interpreter(vm, ctx->pc);

    function->context = NULL;
    function->await = NULL;

    vm->levels[NJS_LEVEL_LOCAL] = cur_local;
    vm->levels[NJS_LEVEL_CLOSURE] = cur_closures;
    vm->levels[NJS_LEVEL_TEMP] = cur_temp;

    vm->top_frame = top;
    vm->active_frame = frame;

    if (ret == NJS_OK) {
        ret = njs_function_call(vm, njs_function(&ctx->capability->resolve),
                            &njs_value_undefined, &vm->retval, 1, &vm->retval);

        njs_async_context_free(vm, ctx);

    } else if (ret == NJS_AGAIN) {
        ret = NJS_OK;

    } else if (ret == NJS_ERROR) {
        if (njs_is_memory_error(vm, &vm->retval)) {
            return NJS_ERROR;
        }

        value = &vm->retval;

        goto failed;
    }

    return ret;

failed:

    (void) njs_function_call(vm, njs_function(&ctx->capability->reject),
                             &njs_value_undefined, value, 1, &vm->retval);

    njs_async_context_free(vm, ctx);

    return NJS_ERROR;
}